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Medical Ethics / Professional Responsibility

Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.

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Showing 190-210 of 3,901 articles
Stepwise decomposition-integration-prediction framework for runoff forecasting considering boundary correction.

Predicting river runoff accurately is of substantial significance for flood control, water resource ...

Apache Spark and Deep Learning Models for High-Performance Network Intrusion Detection Using CSE-CIC-IDS2018.

Keeping computers secure is becoming challenging as networks grow and new network-based technologies...

Research on Named Entity Recognition Based on Multi-Task Learning and Biaffine Mechanism.

Commonly used nested entity recognition methods are span-based entity recognition methods, which foc...

Semantic segmentation method of underwater images based on encoder-decoder architecture.

With the exploration and development of marine resources, deep learning is more and more widely used...

Analysis of the Current Situation of Teaching and Learning of Ideological and Political Theory Courses by Deep Learning.

The objectives are to solve the problems existing in the current ideological and political theory co...

A pretraining domain decomposition method using artificial neural networks to solve elliptic PDE boundary value problems.

Developing methods of domain decomposition (DDM) has been widely studied in the field of numerical c...

Boundary-aware glomerulus segmentation: Toward one-to-many stain generalization.

The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open ne...

Boundary Constraint Network With Cross Layer Feature Integration for Polyp Segmentation.

Clinically, proper polyp localization in endoscopy images plays a vital role in the follow-up treatm...

Boundary Stabilization of Stochastic Delayed Cohen-Grossberg Neural Networks With Diffusion Terms.

This study considers the boundary stabilization for stochastic delayed Cohen-Grossberg neural networ...

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task whi...

Physics-informed neural networks for hydraulic transient analysis in pipeline systems.

In water pipeline systems, monitoring and predicting hydraulic transient events are important to ens...

Improving work detection by segmentation heuristics pre-training on factory operations video.

The measurement of work time for individual tasks by using video has made a significant contribution...

Aerial-aquatic robots capable of crossing the air-water boundary and hitchhiking on surfaces.

Many real-world applications for robots-such as long-term aerial and underwater observation, cross-m...

Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning.

This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U...

Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks.

Recently, heatmap regression has been widely explored in facial landmark detection and obtained rema...

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network.

With the continuous development of imaging sensors, images contain more and more information, the im...

Multi-fidelity information fusion with concatenated neural networks.

Recently, computational modeling has shifted towards the use of statistical inference, deep learning...

MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network.

BACKGROUND: The digital pathology images obtain the essential information about the patient's diseas...

Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.

PURPOSE: Orthognathic surgery requires an accurate surgical plan of how bony segments are moved and ...

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation.

Most methods for medical image segmentation use U-Net or its variants as they have been successful i...

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